论文标题
当压缩学习失败时：责怪解码器还是草图？
When compressive learning fails: blame the decoder or the sketch?
论文作者
论文摘要
在压缩学习中，从草图向量中学到了混合模型（一组质心或高斯混合物），该模型是数据集的高度压缩表示。这需要解决非凸优化问题，因此在实践中使用近似启发式方法（例如Clompr）。在这项工作中，我们通过数值模拟探讨了该非凸优化景观和这些启发式方法的性质。
In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the dataset. This requires solving a non-convex optimization problem, hence in practice approximate heuristics (such as CLOMPR) are used. In this work we explore, by numerical simulations, properties of this non-convex optimization landscape and those heuristics.
